title: Threshold choices of Huber regularization using global- and local-edge-detecting operators for X-ray computed tomographic reconstruction.
abstract: Statistical iterative reconstruction (SIR) approaches have shown great potential in x-ray computed tomographic (CT) reconstruction in the case of low-dose protocol. For yielding high quality image, an edge-preserving regularization should be incorporated into the objective function of SIR approaches. A typical example is the Huber regularization with an edge-preserving non-quadratic potential function which increases less rapidly than the quadratic potential function for sufficiently large arguments. However, a major drawback of the Huber regularization is the determining the threshold, which precludes its extensive applications. In this paper, we investigate both global- and local- edge-detecting operators for threshold choices of Huber regularization and apply them to SIR CT image reconstruction with low-dose scan protocol. Experiments were performed on XCAT phantom by using a CT simulator to obtain the low-dose projection data.
authors.forename: Hua
authors.initials: H
authors.lastname: Zhang
authors.forename: Shanli
authors.initials: S
authors.lastname: Zhang
authors.forename: Debin
authors.initials: D
authors.lastname: Hu
authors.forename: Dong
authors.initials: D
authors.lastname: Zeng
authors.forename: Zhaoying
authors.initials: Z
authors.lastname: Bian
authors.forename: Lijun
authors.initials: L
authors.lastname: Lu
authors.forename: Jianhua
authors.initials: J
authors.lastname: Ma
authors.forename: Jing
authors.initials: J
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D016018:Least-Squares Analysis; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted; D014057:Tomography, X-Ray Computed / Q000379:methods*